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Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, Piotr Chudzik, Karol Tatar, Grzegorz Lisowski, Volodymyr Mosorov e Dominik Sankowski. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences". Applied Sciences 11, n. 10 (19 maggio 2021): 4625. http://dx.doi.org/10.3390/app11104625.
Abstract (sommario):
This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.
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Arifin, Fatchul, Tri Arief Sardjono e Mauridhi Hery Purnomo. "THE RELATIONSHIP BETWEEN ELECTROMYOGRAPHY SIGNAL OF NECK MUSCLE AND HUMAN VOICE SIGNAL FOR CONTROLLING LOUDNESS OF ELECTROLARYNX". Biomedical Engineering: Applications, Basis and Communications 26, n. 05 (26 settembre 2014): 1450054. http://dx.doi.org/10.4015/s1016237214500549.
Abstract (sommario):
Human voice intonation is affected by pitch and loudness. Pitch is related to the frequency of human voice, while loudness is related to the magnitude of human voice. Someone who does not have vocal cords, has no ability to produce voice. This problem is suffered by laryngectomy patients. Over half of all laryngectomy patients worldwide use electrolarynx for the rehabilitation of their speech ability. Unfortunately, the electrolarynx voice produces monotonic and flat intonation. Small changes in pitch and loudness of electrolarynx will give a better expression in laryngectomy patients. However, previous researches have focused on utilization of electromyography (EMG) signal of neck muscle for only pitch control. In this research, the relationship between human voice intonation (i.e. frequency and magnitude) and EMG signals of neck muscles was studied by looking for their correlation and their mutual information. Human voice signal and EMG signal of neck muscle were recorded simultaneously while subjects were saying "A" with varying intonation. The EMG signal of neck muscle was processed using amplifying, filtering, rectifying and "moving average" process. On the other hand, the human voice was processed by FFT Algorithm to obtain magnitude and fundamental frequency. The result shows that the correlation coefficient between human voice magnitudes and EMG signal of neck muscle is 0.93, while the correlation coefficient between human voice frequency and EMG signal of neck muscle is 0.88. Moreover, the mutual information between human voice magnitudes and EMG signal of neck muscle is 1.07, while the mutual information between human voice frequency and EMG signal of neck muscle is 0.65. These results show that the relationship between human voice magnitudes and EMG signal of neck muscle is stronger than the relationship between human voice frequencies and EMG signal of neck muscle. Therefore, it is more appropriate to use the EMG signal of neck muscle for controlling loudness of electrolarynx than that of the pitch of electrolarynx.
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Merletti, R., B. Indino, T. Graven-Nielsen e D. Farina. "Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals". Methods of Information in Medicine 43, n. 01 (2004): 30–35. http://dx.doi.org/10.1055/s-0038-1633419.
Abstract (sommario):
Summary Objectives: Surface EMG crosstalk is the EMG signal detected over a non-active muscle and generated by a nearby muscle. The aim of this study was to analyze the sources of crosstalk signals in surface EMG recordings and to discuss methods proposed in the literature for crosstalk quantification and reduction. Methods: The study is based on both simulated and experimental signals. The simulated signals are generated by a structure based surface EMG signal model. Signals were recorded with both intramuscular and surface electrodes and single motor unit surface potentials were extracted with the spike triggered averaging approach. Moreover, surface EMG signals were recorded from electrically stimulated muscles. Results: From the simulation and experimental analysis it was clear that the main determinants of crosstalk are non-propagating signal components, generated by the extinction of the intracellular action potentials at the tendons. Thus, crosstalk signals have a different shape with respect to the signals detected over the active muscle and contain high frequency components. Conclusions: Since crosstalk has signal components different from those dominant in case of detection from near sources, commonly used methods to quantify and reduce crosstalk, such as the cross-correlation coefficient and high-pass temporal filtering, are not reliable. Selectivity of detection systems must be discussed separately as selectivity with respect to propagating and non-propagating signal components. The knowledge about the origin of crosstalk signal constitutes the basis for crosstalk interpretation, quantification, and reduction.
4
Shiao, Yaojung, e Thang Hoang. "Exercise Condition Sensing in Smart Leg Extension Machine". Sensors 22, n. 17 (23 agosto 2022): 6336. http://dx.doi.org/10.3390/s22176336.
Abstract (sommario):
Skeletal muscles require fitness and rehsabilitation exercises to develop. This paper presents a method to observe and evaluate the conditions of muscle extension. Based on theories about the muscles and factors that affect them during leg contraction, an electromyography (EMG) sensor was used to capture EMG signals. The signals were applied by signal processing with the wavelet packet entropy method. Not only did the experiment follow fitness rules to obtain correct EMG signal of leg extension, but the combination of inertial measurement unit (IMU) sensor also verified the muscle state to distinguish the muscle between non-fatigue and fatigue. The results show the EMG changing in the non-fatigue, fatigue, and calf muscle conditions. Additionally, we created algorithms that can successfully sense a user’s muscle conditions during exercise in a leg extension machine, and an evaluation of condition sensing was also conducted. This study provides proof of concept that EMG signals for the sensing of muscle fatigue. Therefore, muscle conditions can be further monitored in exercise or rehabilitation exercise. With these results and experiences, the sensing methods can be extended to other smart exercise machines in the future.
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Neto, Osmar Pinto, e Evangelos A. Christou. "Rectification of the EMG Signal Impairs the Identification of Oscillatory Input to the Muscle". Journal of Neurophysiology 103, n. 2 (febbraio 2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.
Abstract (sommario):
Rectification of EMG signals is a common processing step used when performing electroencephalographic–electromyographic (EEG–EMG) coherence and EMG–EMG coherence. It is well known, however, that EMG rectification alters the power spectrum of the recorded EMG signal (interference EMG). The purpose of this study was to determine whether rectification of the EMG signal influences the capability of capturing the oscillatory input to a single EMG signal and the common oscillations between two EMG signals. Several EMG signals were reconstructed from experimentally recorded EMG signals from the surface of the first dorsal interosseus muscle and were manipulated to have an oscillatory input or common input (for pairs of reconstructed EMG signals) at various frequency bands (in Hz: 0–12, 12–30, 30–50, 50–100, 100–150, 150–200, 200–250, 250–300, and 300–400), one at a time. The absolute integral and normalized integral of power, peak power, and peak coherence (for pairs of EMG signals) were quantified from each frequency band. The power spectrum of the interference EMG accurately detected the changes to the oscillatory input to the reconstructed EMG signal, whereas the power spectrum of the rectified EMG did not. Similarly, the EMG–EMG coherence between two interference EMG signals accurately detected the common input to the pairs of reconstructed EMG signals, whereas the EMG–EMG coherence between two rectified EMG signals did not. The frequency band from 12 to 30 Hz in the power spectrum of the rectified EMG and the EMG–EMG coherence between two rectified signals was influenced by the input from 100 to 150 Hz but not from the input from 12 to 30 Hz. The study concludes that the power spectrum of the EMG and EMG–EMG coherence should be performed on interference EMG signals and not on rectified EMG signals because rectification impairs the identification of the oscillatory input to a single EMG signal and the common oscillatory input between two EMG signals.
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Ojha, Anuj. "An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview". Extensive Reviews 3, n. 1 (31 dicembre 2023): 24–37. http://dx.doi.org/10.21467/exr.3.1.8382.
Abstract (sommario):
Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.
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HAMZI, Maroua, Mohamed BOUMEHRAZ e Rafia HASSANI. "Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features". Electrotehnica, Electronica, Automatica 71, n. 3 (15 agosto 2023): 61–68. http://dx.doi.org/10.46904/eea.23.71.3.1108007.
Abstract (sommario):
Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.
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Pratama, Destra Andika, Yeni Irdayanti e Satrio Aditiyas Sukardi. "EMG Signal Analysis on Flexion Extension Movements of The Hand and Leg Using Matlab". Radiasi : Jurnal Berkala Pendidikan Fisika 16, n. 2 (29 settembre 2023): 61–70. http://dx.doi.org/10.37729/radiasi.v16i2.3373.
Abstract (sommario):
Muscle Spiker Shield is a tool used to record electrical signals generated by the muscles of the human body. These signals can provide important information about the health and activities of organisms, especially humans. As technology advances, more and more devices can be used to record the activity of these signals, including the Muscle Spiker Shield. One of the uses of the Muscle Spiker Shield is to monitor muscle wave activity. Human muscle waves are electrical signals generated by muscles and can provide information about the state of a person's movement activity. Monitoring human muscle wave activity can help in various fields, such as medicine, psychology, and sports. Currently, an electromyograph has been developed which functions as a voltage meter for all muscles to detect muscles in a state of tension and relaxation with the help of a microcontroller. On the Electromyography signal output then to the Arduino uno microcontroller. When using the Muscle Spiker Shield tool with MATLAB, the signals recorded by the tool are imported into the MATLAB software. Then, the data can be processed using various signal analysis techniques, such as filtering, peak detection and statistical processing. Some of the applications that can be done are monitoring leg and hand muscle wave activity during meditation, monitoring muscle wave activity to determine a person's movements, and monitoring muscle wave activity during exercise.
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Rusli, Rusli Ully, Ruslan Ruslan, Sarifin G., Arimbi Arimbi e Mariyal Qibtiyah. "Measurement of Medial Head Gastrocnemius Muscle Contraction Strength in Basic Sepak Takraw Techniques Using Electromyogram Signals". COMPETITOR: Jurnal Pendidikan Kepelatihan Olahraga 15, n. 3 (28 ottobre 2023): 683. http://dx.doi.org/10.26858/cjpko.v15i3.53403.
Abstract (sommario):
This study aims to measure the strength of contraction of the gastrocnemius medial head muscle in basic techniques sepak sila using electromyogram signals. The subjects in this research were 3 South Sulawesi sepak takraw athletes. EMG signal measurement using the Trigno™ Wireless EMG System. The output data is the results of the EMG signal, the Root Mean Square value of each muscle component measured. The data analysis technique uses quantitative descriptive. The results of EMG signal measurements produce RMS values for each muscle measured as follows: (1). The subject produced the largest first EMG signal from the right gastrocnemius medial head muscle, 0.44631mV with an RMS value of 93.009mV, and the largest left gastrocnemius medial head muscle, 0.33889mV with an RMS value of 61.302mV. (2). The subject produced the second largest right gastrocnemius medial head muscle EMG signal of 1.66238mV with an RMS value of 38.7856mV, and the largest left gastrocnemius medial head muscle of 1.37871mV with an RMS value of 25.6827mV. (3). The subject produced the third largest right gastrocnemius medial head muscle EMG signal of 2.02191mV with an RMS value of 76.7969mV, and the largest left gastrocnemius medial head muscle of 0.37397mV with an RMS value of 47.3252mV. It was concluded that the third subject produced the highest muscle EMG strength which occurred in the right gastrocnemius medial head muscle signal and the smallest EMG signal in the left gastrocnemius medial head.
10
Liang, Hongbo, Yingxin Yu, Mika Mochida, Chang Liu, Naoya Ueda, Peirang Li e Chi Zhu. "EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs". Symmetry 12, n. 11 (10 novembre 2020): 1851. http://dx.doi.org/10.3390/sym12111851.
Abstract (sommario):
Brain–Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder’s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor–extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder’s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (±0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.
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Tanuja Subba, Et al. "A Study on Electromyography Signal as a Controller". International Journal on Recent and Innovation Trends in Computing and Communication 11, n. 9 (13 febbraio 2024): 4662–67. http://dx.doi.org/10.17762/ijritcc.v11i9.10014.
Abstract (sommario):
Human computer interaction (HCI) is the study of interfaces between human and computer. When an input keyboard is pressed the output is displayed in the monitor is a simple example of human and computer interaction. World Wide Web is yet another example of HCI. HCI is everywhere and has become an important aspect in human life. HCI have many subfields and one among them is the study of biosignals. Signals that are generated from living body during muscle contraction, eye movement, brain signal are biosignals and these signals have potential for developing an interface for human computer interaction. There are many such bio electric signals which can be made to use for developing interface and that can be done by acquiring these signals which will form a linkage with the computer technique. These types of signals are brain signal called Electroencephalogram (EEG), heart signal Electrocardiogram (ECG), eye movement signal Electrooculogram (EOG) and muscle signalElectromyogram (EMG). The paper focuses on the study of muscle signal controller as HCI, EMG signals are captured during contraction of a skeletal muscle. The signal is then amplified and converted into usable signals that will be fed as an input to computer and can be used for controlling certain devices.
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Song, Kwangsub, Sangui Choi e Hooman Lee. "Voluntary Muscle Contraction Detection Algorithm Based on LSTM for Muscle Quality Measurement Algorithm". Applied Sciences 11, n. 18 (17 settembre 2021): 8676. http://dx.doi.org/10.3390/app11188676.
Abstract (sommario):
In this paper, we propose the long–short-term memory (LSTM)-based voluntary and non-voluntary (VNV) muscle contraction classification algorithm in an electrical stimulation (ES) environment. In order to measure the muscle quality (MQ), we employ the non-voluntary muscle contraction signal, which occurs by the ES. However, if patient movement, such as voluntary muscle contractionm, occurs during the ES, the electromyography (EMG) sensor captures the VNV muscle contraction signals. In addition, the voluntary muscle contraction signal is a noise component in the MQ measurement technique, which uses only non-voluntary muscle contraction signals. For this reason, we need the VNV muscle contraction classification algorithm to classify the mixed EMG signal. In addition, when recording EMG while using the ES, the EMG signal is significantly contaminated due to the ES signal. Therefore, after we suppress the artifact noise, which is contained in the EMG signal, we perform VNV muscle contraction classification. For this, we first eliminate the artifact noise signal using the ES suppression algorithm. Then, we extract the feature vector, and then the feature vector is reconstructed through the feature selection process. Finally, we design the LSTM-based classification model and compare the proposed algorithm with the conventional method using the EMG data. In addition, to verify the performance of the proposed algorithm, we quantitatively compared results in terms of the confusion matrix and total accuracy. As a result, the performance of the proposed algorithm was higher than that of the conventional methods, including the support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN).
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Caesaria, Arifah Putri, Endro Yulianto, Sari Luthfiyah, Triwiyanto Triwiyanto e Achmad Rizal. "Effect of Muscle Fatigue on EMG Signal and Maximum Heart Rate for Pre and Post Physical Activity". Journal of Electronics, Electromedical Engineering, and Medical Informatics 5, n. 1 (30 gennaio 2023): 39–45. http://dx.doi.org/10.35882/jeeemi.v5i1.278.
Abstract (sommario):
Sport is a physical activity that can optimize body development through muscle movement. Physical activity without rest with strong and prolonged muscle contractions results in muscle fatigue. Muscle fatigue that occurs causes a decrease in the work efficiency of muscles. Electrocardiography (ECG) is a recording of the heart's electrical activity on the body's surface. EMG is a technique for measuring electrical activity in muscles. This study aims to detect the effect of muscle fatigue on cardiac signals by monitoring ECG and EMG signals. This research method uses the Maximum Heart Rate with a research design of one group pre-test-post-test. The independent variable is the ECG signal when doing plank activities, while the dependent variable is the result of monitoring the ECG signal. To get the Maximum Heart Rate results, respondents use the Karnoven formula and perform the T-test. Test results show a significant value (pValue <0.05) in pre-exercise and post-exercise. When the respondent experiences muscle fatigue, it shows the effect of changes in the shape of the ECG signal which is marked by the presence of movement artifact noise. It concluded that the tools in this study can be used properly. This study has limitations including noise in the AD8232 module circuit and the display on telemetry where the width of the box cannot be adjusted according to the ECG paper.is It recommended for further research to use components with better quality and replace the display using the Delphi interface.
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Anas Fouad Ahmed. "A quick survey of filtering techniques for surface electromyography signals". Global Journal of Engineering and Technology Advances 11, n. 3 (30 giugno 2022): 105–10. http://dx.doi.org/10.30574/gjeta.2022.11.3.0101.
Abstract (sommario):
Electromyography (EMG) represents the electrical activity of muscles, and it has a wide range of usage in biomedical and clinical tasks. During myoelectrical stimulation, the EMG signal has two sources: the meaningful electrical response of the muscles and signal noise. Technical noise (such as power line noise) and biological noise (ECG). The noises in the system must be efficiently rejected, as this will disturb the analysis of the activity of the muscle. This paper presents different types of noise that corrupt the EMG signal and the main denoising approaches for minimizing the noise effect.
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JUNG, CHAN YONG, JUN-SIK PARK, YONGHYUN LIM, YOUNG-BEOM KIM, KWAN KYU PARK, JE HEON MOON, JOO-HO SONG e SANGHOON LEE. "ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN". Journal of Mechanics in Medicine and Biology 18, n. 05 (agosto 2018): 1850042. http://dx.doi.org/10.1142/s0219519418500422.
Abstract (sommario):
This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG signal. This work presents a global EMG index map by using the new analytical technique based on the relationship between the average IEMG and mean power frequency (MPF) values. The proposed method enables simultaneous estimation of muscle fatigue level and force using real-time EMG signals from the femoral and gastrocnemius muscles.
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Malik Mohd Ali, Abdul, Syed Faiz Ahmed, Athar Ali, M. Kamran Joyo, Kushairy A. Kadir e Radzi Ambar. "EMG-Based Spasticity Robotic Arm Forupper Arm Fatigue Identification". International Journal of Engineering & Technology 7, n. 2.34 (8 giugno 2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.34.13917.
Abstract (sommario):
Electromyogram (EMG) signal reflect the electrical activity of human muscle and contains information about the structure of muscle. Furthermore, motor unit action potential (MUAP) is the results from spatial and temporal summation of difference muscle fibers of a single motor. The EMG signal results, in turn is from the summation of different MUAPs which are sufficiently near the recording electrode. EMG signal can identify the differences between signals from bicep, triceps and forearms during exercise. Raw data from the experiment is vital to assist physiotherapy to understand when the subject fatigue of noise high pick signal during rehabilitation. Several normal subjects were selected to perform experiments to understand the pattern of fatigue in early state, middle stage and last stage of exercises.
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Lima Alberton, Cristine, Stephanie Santana Pinto, Natália Amélia da Silva Azenha, Eduardo Lusa Cadore, Marcus Peikriszwili Tartaruga, Bruno Brasil e Luiz Fernando Martins Kruel. "Kinesiological Analysis of Stationary Running Performed in Aquatic and Dry Land Environments". Journal of Human Kinetics 49, n. 1 (1 dicembre 2015): 5–14. http://dx.doi.org/10.1515/hukin-2015-0103.
Abstract (sommario):
Abstract The purpose of the present study was to analyze the electromyographic (EMG) signals of the rectus femoris (RF), vastus lateralis (VL), semitendinosus (ST) and short head of the biceps femoris (BF) during the performance of stationary running at different intensities in aquatic and dry land environments. The sample consisted of 12 female volunteers who performed the stationary running exercise in aquatic and dry land environments at a submaximal cadence (80 beats·min-1 controlled by a metronome) and at maximal velocity, with EMG signal measurements from the RF, VL, ST and BF muscles. The results showed a distinct pattern between environments for each muscle examined. For the submaximal cadence of 80 beats·min-1, there was a reduced magnitude of the EMG signal in the aquatic environment, except for the ST muscle, the pattern of which was similar in both environments. In contrast to the submaximal cadence, the pattern of the EMG signal from all of the muscles showed similar magnitudes for both environments and phases of movement at maximal velocity, except for the VL muscle. Therefore, the EMG signals from the RF, VL, ST and BF muscles of women during stationary running had different patterns of activation over the range of motion between aquatic and dry land environments for different intensities. Moreover, the neuromuscular responses of the lower limbs were optimized by an increase in intensity from submaximal cadence to maximal velocity.
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Fauzi, Muhammad, Endro Yulianto, Bambang Guruh Irianto, Sari Luthfiyah, Triwiyanto Triwiyanto, Vishwajeet Shankhwar e Bahaa Eddine ELBAGHAZAOUI. "Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram (EMG Parameter ) Monitoring Signals". Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 4, n. 3 (23 agosto 2022): 114–22. http://dx.doi.org/10.35882/ijeeemi.v4i3.240.
Abstract (sommario):
Physical activity is an activity of body movement by utilizing skeletal muscles that is carried out daily. One form of physical activity is an exercise that aims to improve health and fitness. Parameters related to health and fitness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. To measure muscle fatigue, the authors used electromyographic (EMG) signals through monitoring changes in muscle electrical activity. This study aims to make a tool to detect the effect of muscle fatigue on cardiac signals on physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. To get more detailed measurement results, the authors use MPF, MDF and MNF and perform a T-test. The test results showed a significant value (pValue <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628 which indicates there is a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the tools in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.
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Prasad, V. V. K. D. V., B. Nagasirisha, Joycy Y. Janitha, Naik R. Venkatesh, Naga Sai B. Lalithadithya e T. Ramya. "Feature extraction and classification of different hand movements from the emg signal using linear discriminant analysis classifier". i-manager’s Journal on Electronics Engineering 14, n. 2 (2024): 19. http://dx.doi.org/10.26634/jele.14.2.20585.
Abstract (sommario):
In biomedical research, Electromyography (EMG) data play a crucial role as a bridge between human motions and machine interpretation, offering valuable insights into muscle activation. EMG signals give vital information on hand movements in the context of applications like gesture recognition, prosthetic control, and rehabilitation. This paper describes the classification of EMG signals based on muscle motions, which makes it simpler to identify distinct gestures or movements. A Linear Discriminant Analysis (LDA) classifier is used to differentiate between various classes of muscle activity. In order to record EMG signals during hand motions, surface electrodes are carefully positioned on pertinent muscles. Muscle activity may be tracked in real time with these non-invasive electrodes. In order to extract meaningful information from these signals, which are complex and frequently contaminated by noise, strong feature extraction techniques are needed. When working with noisy signals, denoising is a commonly used approach to restoring the original quality of the source data. It attempts to maintain relevant information by reducing noise in the raw EMG signals. In order to retrieve only the pertinent information from the original EMG signal data, any unnecessary noise must first be removed. Through the identification of key characteristics in the time, frequency, and time-frequency domains, it transforms unstructured EMG data. This procedure improves the next step of classification, which is the identification and classification of patterns in the EMG signals. Ultimately, the obtained information is employed to classify signals by the Linear Discriminant Analysis (LDA) classifier, demonstrating a distinction between various muscle motions with over 80% accuracy.
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Huang, Q. H., Y. P. Zheng, X. Chena, J. F. He e J. Shi. "A System for the Synchronized Recording of Sonomyography, Electromyography and Joint Angle". Open Biomedical Engineering Journal 1, n. 1 (11 dicembre 2007): 77–84. http://dx.doi.org/10.2174/1874120700701010077.
Abstract (sommario):
Ultrasound and electromyography (EMG) are two of the most commonly used diagnostic tools for the assessment of muscles. Recently, many studies reported the simultaneous collection of EMG signals and ultrasound images, which were normally amplified and digitized by different devices. However, there is lack of a systematic method to synchronize them and no study has reported the effects of ultrasound gel to the EMG signal collection during the simultaneous data collection. In this paper, we introduced a new method to synchronize ultrasound B-scan images, EMG signals, joint angles and other related signals (e.g. force and velocity signals) in real-time. The B-mode ultrasound images were simultaneously captured by the PC together with the surface EMG (SEMG) and the joint angle signal. The deformations of the forearm muscles induced by wrist motions were extracted from a sequence of ultrasound images, named as Sonomyography (SMG). Preliminary experiments demonstrated that the proposed method could reliably collect the synchronized ultrasound images, SEMG signals and joint angle signals in real-time. In addition, the effect of ultrasound gel on the SEMG signals when the EMG electrodes were close to the ultrasound probe was studied. It was found that the SEMG signals were not significantly affected by the amount of the ultrasound gel. The system is being used for the study of contractions of various muscles as well as the muscle fatigue.
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TSUJI, TOSHIO, NAN BU, JUN ARITA e MAKOTO OHGA. "A SPEECH SYNTHESIZER USING FACIAL EMG SIGNALS". International Journal of Computational Intelligence and Applications 07, n. 01 (marzo 2008): 1–15. http://dx.doi.org/10.1142/s1469026808002119.
Abstract (sommario):
This paper proposes a novel phoneme classification method using facial electromyography (EMG) signals. This method makes use of differential EMG signals between muscles for phoneme classification, which enables a speech synthesizer to be constructed using fewer electrodes. The EMG signal is derived as a differential between monopolar electrodes attached to two different muscles, unlike conventional methods in which the EMG signal is derived as a differential between bipolar electrodes attached to the same muscle. Frequency-based feature patterns are then extracted using a filter bank, and the phonemes are classified using a probabilistic neural network, called a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN). Since RD-LLGMN merges feature extraction and pattern classification processes into a single network structure, a lower-dimensional feature set that is consistent with classification purposes can be extracted; consequently, classification performance can be improved. Experimental results indicate that the proposed method with a fewer number of electrodes can achieve a considerably high classification accuracy.
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Qassim, Hassan M., Wan Zuha Wan Hasan, Hafiz R. Ramli, Hazreen Haizi Harith, Liyana Najwa Inche Mat e Luthffi Idzhar Ismail. "Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier". Sensors 22, n. 5 (28 febbraio 2022): 1900. http://dx.doi.org/10.3390/s22051900.
Abstract (sommario):
The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.
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Triwiyanto, Triwiyanto, Triana Rahmawati, I. Putu Alit Pawana e Evrinka Hikaristiana Maulidia. "Investigation of Electrode Location to Improve the Accuracy of Wearable Hand Exoskeleton Trainer Based on Electromyography". Journal of Biomimetics, Biomaterials and Biomedical Engineering 55 (28 marzo 2022): 71–80. http://dx.doi.org/10.4028/p-y7g473.
Abstract (sommario):
EMG signal has a random and stochastic characteristics, so it is difficult to predict the amplitude. Furthermore, the EMG signal depends on the electrodes location. Therefore, a proper muscle selection determines the system's accuracy value. The purpose of this study was to investigate the exact location of the electrodes to improve the accuracy of the wearable hand exoskeleton trainer based on electromyography (EMG) signal control. The main advantage of the results of this study is that the most dominant muscle was found in the development of a wearable hand exoskeleton based on an EMG signal threshold. Therefore, the model can be controlled using a single electrode pair which can further be applied using a low-cost microcontroller. In this study, ten respondents were involved in the data acquisition. The discovery of the dominant muscle was carried out by investigating the dominant EMG signal in three muscles (Abductor pollicis longus, extensor digitorum) that plays a role in the open and close movements of the hand exoskeleton. Dry electrode was used to detect EMG signal activity on the skin surface. The EMG signal was then extracted using the root mean square (RMS) feature. After the evaluation, the results showed that the flexor digitorum superficialis muscle in the rest position produced higher accuracy value than the other muscles, which was 96.63±0.67%. In the implementation, the product of this research can be applied for rehabilitation steps in post-stroke patients which is carried out either in a medical rehabilitation unit or at home independently.
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Sarangi, Animesh, Bal Gopal Mishra e Satyabhama Dash. "Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal". YMER Digital 21, n. 08 (11 agosto 2022): 400–407. http://dx.doi.org/10.37896/ymer21.08/36.
Abstract (sommario):
Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are then calculated using singular value decomposition(SVD) from the multichannel data's covariance matrix. A threshold is selected to locate these eigenvectors, which are utilized to generate the required subspace. After locating the subspace, the multichannel data is simply projected into it, followed by a method called diagonal averaging which will create the original time series and extract the ECG signals. Keywords: Electrocardiogram, EMG artifact, Singular Spectrum Analysis, Embedding, SVD, Mobility.
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KEERATIHATTAYAKORN, Saran, e Shigeru TADANO. "1B08 Relationship between EMG signal and muscle acceleration during elbow flexion/extension". Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2013.25 (2013): 73–74. http://dx.doi.org/10.1299/jsmebio.2013.25.73.
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GAO, YONGSHENG, SHENGXIN WANG, FEIYUN XIAO e JIE ZHAO. "AN ANGLE-EMG BIOMECHANICAL MODEL OF THE HUMAN ELBOW JOINT". Journal of Mechanics in Medicine and Biology 16, n. 06 (settembre 2016): 1650078. http://dx.doi.org/10.1142/s0219519416500780.
Abstract (sommario):
The biomechanical model of the human elbow joint is extensively studied. In the model, the surface electromyography (sEMG) is used as the input signal, whereas the muscle force or muscle torque is commonly considered as the output signal. The estimation of the actual muscle force or torque is important to effectively modulate the tremor suppression. However, the measurement of the muscle force or torque in vivo is difficult. In this paper, a new angle-to-EMG biomechanical model of the elbow joint was developed and evaluated by comparing the measured sEMG with the calculated sEMG. Three sources of the sEMG signal, namely, the central nervous system (CNS), the Golgi tendon and the muscle spindle were considered in this model. Furthermore, a local PID algorithm was proposed to describe the impact of the CNS on the motor neuron and the Golgi tendon model was used to transform muscle forces to stimulus signals. The model was calibrated by an improved search procedure combining the Powell search and the direct search to determine optimal model parameters. In the experiment, an sEMG signal acquisition system was established to measure the sEMG signal and the elbow joint angle. The experimental results, the predicted sEMG signal well following the measured sEMG, demonstrated that the calibrated model could be used to estimate in vivo sEMG signals and is beneficial to explore the peripheral neural system and the pathogenesis of tremor.
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Sadikoglu, Fahreddin, Cemal Kavalcioglu e Berk Dagman. "Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease". Procedia Computer Science 120 (2017): 422–29. http://dx.doi.org/10.1016/j.procs.2017.11.259.
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Putra, Darma Setiawan, e Yuril Umbu WW. "Feature Extraction of Facial Electromyograph (EMG) Signal for Aceh Languages Speech using Discrete Wavelet Transform (DWT)". Jurnal Inotera 4, n. 1 (10 luglio 2019): 31. http://dx.doi.org/10.31572/inotera.vol4.iss1.2019.id73.
Abstract (sommario):
The facial electromyograph (FEMG) signal is a signal that occurs in the muscles of the contracted human face. This FEMG signal is one of the techniques used to study human speech recognition. It can be acquired by placing an electrode surface on the skin around the facial articulation muscle. Three types of muscles in this study are the masseter, risorius and depressor muscle. This study aims to extract and analyze the features in the FEMG signal. The extraction method is the discrete wavelet transform (DWT). The type of wavelet transform is Daubechies2 with level 5. After extraction and analysis of FEMG signals, the FEMG signal pattern for each spoken word indicated by differences in the approximation and detail coefficient of the FEMG signal. In addition, the level of difference in the FEMG signal pattern is also indicated by the histogram of the approximation coefficient of the FEMG signal. Thus, the discrete wavelet transform method can be used as one of the methods for extracting the FEMG signal feature in a human facial electromyograph (FEMG) signal.
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Isezaki, Takashi, Hideki Kadone, Arinobu Niijima, Ryosuke Aoki, Tomoki Watanabe , Toshitaka Kimura e Kenji Suzuki. "Sock-Type Wearable Sensor for Estimating Lower Leg Muscle Activity Using Distal EMG Signals". Sensors 19, n. 8 (25 aprile 2019): 1954. http://dx.doi.org/10.3390/s19081954.
Abstract (sommario):
Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prevent electrode displacement. This makes it difficult for users to wear such measurement systems. A less restraining wearable system, wherein the electrodes are placed around the ankle, is realized for target muscles widely distributed around the shank. The signals obtained from around the ankle are propagated biosignals from several muscles, and are referred to as distal EMG signals. Our objective is to develop a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals. We propose a signal processing method based on multiple bandpass filters from the perspectives of noise separation and feature augmentation. We conducted an experiment for designing the hardware configuration, and three other experiments for evaluating the estimation accuracy and dependability of muscle activity analysis. Compared to the baseline based on a 20-500 Hz bandpass filter, the results indicated that the proposed system estimates muscle activity with higher accuracy. Experimental results suggest that lower leg muscle activity can be estimated using distal EMG signals.
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Kamal, Shahul Mujib, Sue Sim, Rui Tee, Visvamba Nathan e Hamidreza Namazi. "Complexity-Based Analysis of the Relation between Human Muscle Reaction and Walking Path". Fluctuation and Noise Letters 19, n. 03 (28 gennaio 2020): 2050025. http://dx.doi.org/10.1142/s021947752050025x.
Abstract (sommario):
Legs are the contact point of humans during walking. In fact, leg muscles react when we walk in different conditions (such as different speeds and paths). In this research, we analyze how walking path affects leg muscles’ reaction. In fact, we investigate how the complexity of muscle reaction is related to the complexity of path of movement. For this purpose, we employ fractal theory. In the experiment, subjects walk on different paths that have different fractal dimensions and then we calculate the fractal dimension of Electromyography (EMG) signals obtained from both legs. The result of our analysis showed that the complexity of EMG signal increases with the increment of complexity of path of movement. The conducted statistical analysis also supported the result of analysis. The method of analysis used in this research can be further applied to find the relation between complexity of path of movement and other physiological signals of humans such as respiration and Electroencephalography (EEG) signal.
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Deslivia, Maria, Hyun-Joo Lee, Rizki Zulkarnain, Bin Zhu, Arnold Adikrishna, In-ho Jeon e Keehoon Kim. "The Effect of Split Nerve on Electromyography Signal Pattern in a Rat Model". Journal of Reconstructive Microsurgery 34, n. 02 (26 settembre 2017): 095–102. http://dx.doi.org/10.1055/s-0037-1606539.
Abstract (sommario):
Background Recent developments of prosthetic arm are based on the use of electromyography (EMG) signals. To provide improvements, such as coordinated movement of multiple joints and greater control intuitiveness, higher variability of EMG signals is needed. By splitting a nerve lengthwise, connecting each half to new target muscles, and employing a program to assign each biosignal pattern to a specific movement, we hope to enrich the number of biosignal sites on amputees' stump. Methods We split the gastrocnemius muscle of 12 Sprague-Dawley rats into two muscle heads, searched for the peroneal nerve, divided them lengthwise, and connected one half of the nerve to the tibial nerve innervating both muscle heads (SN_50, n = 8). In another group, we connected the undivided peroneal nerve to the nerve of a single muscle head (non-SN_100, n = 6), while the other muscle head received different innervation (non-SN_0, n = 6). After 10 weeks, we stimulated the peroneal nerve and measured the EMG amplitude. Results Mean EMG amplitude of the muscle head innervated by one half of the nerve (SN_50; 1.77 [range: 0.71–3.24] mV) and by the undivided nerve (non-SN_100; 3.45 mV [range: 1.13–5.34]) was not significantly different. However, the mean EMG amplitude produced by SN_50 was significantly different from that of the other innervation (i.e., non-SN_0; 0.76 mV [range: 0.41–1.35]), indicating the presence of noise. Conclusion Split nerve in combination with split-muscle procedure can yield a meaningful EMG signal that might be used to convey the intention of living organism to a machine.
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Jeon, Bu Il, Byung Jun Kang, Hyun Chan Cho e Jongwon Kim. "Motion Recognition and an Accuracy Comparison of Left and Right Arms by EEG Signal Analysis". Applied Sciences 9, n. 22 (14 novembre 2019): 4885. http://dx.doi.org/10.3390/app9224885.
Abstract (sommario):
An electromyogram (EMG) is a signal for muscle output that indicates the degree of muscle contraction and relaxation. For these muscle signals to be output, certain signals must be received from the brain. To analyze these relations, electroencephalograms (EEGs) of the brain are measured to extract brain waves that are active at that time, although it is difficult to identify or distinguish expression patterns of the brain signal through EMG output. However, the brain signal operates via a partially reached signal and transmits the results of the operation. In this study, we analyze signals transmitted in this process and confirm whether human motion can be predicted from brain signals. It is not easy to guess the exact protocol of the brain using a general method, because a biosignal is a signal that differs from person to person. However, by analyzing the signals displayed by a particular user through actions, it is possible to determine the presence or absence of a signal to distinguish muscle movements. In the course of signal transduction, the energy of the left and right brain waves changes in the form of energy or signals that cause an arm’s movement. Responding to this, we analyze the signal transmission process of brain signals and EMGs to analyze loss and generated output. We extract EEG data from brain waves and determine EMG signals from the energy characteristics; we then collect and merge the results of spectra analysis through the Common Spatial Pattern (CSP) filter and explore the basis for predicting wills during muscle signals and stimulation transmission. The active information of the data within the working time of left and right brain waves depends on the changes of the left and right brain waves. It is proposed that the appearance of similar signals at these specific timescales can help identify the operations of the arms and outputs by the left and right biceps.
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PHINYOMARK, ANGKOON, FRANCK QUAINE, YANN LAURILLAU, SIRINEE THONGPANJA, CHUSAK LIMSAKUL e PORNCHAI PHUKPATTARANONT. "EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE". Fluctuation and Noise Letters 12, n. 03 (settembre 2013): 1350016. http://dx.doi.org/10.1142/s0219477513500168.
Abstract (sommario):
To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.
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Chen, Wei, Ruizhi Chen, Xiang Chen, Xu Zhang, Yuwei Chen, Jianyu Wang e Zhongqian Fu. "Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning". Journal of Navigation 64, n. 2 (2 marzo 2011): 265–80. http://dx.doi.org/10.1017/s0373463310000391.
Abstract (sommario):
In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.
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Mukhtar Alam, Mohd, e Abid Ali Khan. "Electromyography-based Fatigue Assessment During Endurance Testing by Different Vibration Training Protocols". Iranian Rehabilitation Journal 19, n. 1 (1 marzo 2021): 85–98. http://dx.doi.org/10.32598/irj.19.1.1150.1.
Abstract (sommario):
Objectives: This study presents a method of assessing muscle fatigue during endurance testing at 50% maximal voluntary contraction (MVC) using electromyography (EMG) information as indirect indices of fatigability in the forearm muscles, namely, flexor digitorum superficialis (FDS); flexor carpi ulnaris (FCU); extensor carpi ulnaris (ECU) and extensor carpi radialis brevis (ECRB)." This study presents a method of assessing muscle fatigue during endurance testing at 50% maximal voluntary contraction (MVC) using electromyography (EMG) information as indirect indices of fatigability in the forearm muscles, namely, flexor digitorum superficialis (FDS); flexor carpi ulnaris (FCU); extensor carpi ulnaris (ECU) and extensor carpi radialis brevis (ECRB). Methods: A randomized comparative experimental design was used during endurance test with 8 VT protocols; based on different combinations of vibration frequency (35 & 45 Hz), amplitude (3±0.5g & 9±0.5g), and exposure duration (30 & 60 seconds), i.e., were given to the study participants for 4 days. A random sampling of participants was conducted from two groups (n=56/group), as follows: individuals with a Sedentary Lifestyle (SL) and a group of Construction Workers (CW). Results: Multivariate Analysis of Variance (MANOVA) results indicated a significant increase in EMG rms, median frequency, waveform length, mean absolute value (P<0.001), and the variance of EMG signal (P<0.05) (except in ECU for the SL group and ECRB for the CW group on the variance of EMG signal) after VT in all muscles of both research groups. Therefore, an increase in the EMG parameter value after a grip endurance task revealing an elevation in EMG signal amplitude is indicative of muscle fiber fatigue. Furthermore, the strongest correlation was found between grip endurance and WL (r=0.471, P<0.001), and EMG rms (r=0.401, P<0.001) of the ECU muscle in the SL group only. Discussion: The patterns of EMG signal represented the amplitude and spectral parameters of the signal, enabling real-time fatigue analysis. Additionally, surface EMG information is useful for indirectly evaluating performance fatigue during the endurance test.
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Soundirarajan, Mirra, Mohammad Hossein Babini, Sue Sim, Visvamba Nathan e Hamidreza Namazi. "Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli". Fluctuation and Noise Letters 19, n. 04 (23 giugno 2020): 2050041. http://dx.doi.org/10.1142/s0219477520500418.
Abstract (sommario):
In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of analysis and showed that visual stimulus with greater complexity has greater effect on the complexity of EEG and EMG signals. Therefore, we showed the relationship between facial muscles and brain activities in this paper. The method of analysis in this research can be further employed to investigate the relationship between other human organs’ activities and brain activity.
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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad e Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network". Future Internet 11, n. 1 (21 gennaio 2019): 25. http://dx.doi.org/10.3390/fi11010025.
Abstract (sommario):
Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.
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Lin, B., S. F. Wong e A. Baca. "Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Study". International Journal of Computer Science in Sport 17, n. 1 (1 luglio 2018): 77–93. http://dx.doi.org/10.2478/ijcss-2018-0004.
Abstract (sommario):
Abstract The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions
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Patel, Shubha V., e S. L. Sunitha. "Analysis of Muscular Paralysis using EMG Signal with Wavelet Decomposition Approach". Asian Journal of Computer Science and Technology 11, n. 1 (1 giugno 2022): 5–16. http://dx.doi.org/10.51983/ajcst-2022.11.1.3241.
Abstract (sommario):
Paralysis refers to temporary or permanent loss of voluntary muscle movement in a body part or region. The degree of muscle function loss determines the severity of paralysis. The muscle function is represented by electrical activity of the muscles. Electromyography is a technique concerned with the analysis of myoelectric signals. EMG allows the determination of muscular activity. EMG signal analysis is performed using the features extracted in time domain, frequency domain and time frequency domain. In this work, the EMG of Amyotrophic Lateral Sclerosis (ALS), Myopathy, and Normal conditions are considered, and the time frequency analysis has been carried out to extract the features using wavelet decomposition approach. The classification of normal and paralyzed condition is carried by four classifier models. The classifier models used are Multi-layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and Nearest Neighbor (NN) models. The standard data set has been used for the purpose. The classification accuracy obtained for MLP is 80%, for RF is 75%, for GB is 79%, and for NN is 69%. MLP show better classification performance over RF, GB, and NN Classifiers.
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Tahan, Nahid, Amir Massoud Arab, Bita Vaseghi e Khosro Khademi. "Electromyographic Evaluation of Abdominal-Muscle Function With and Without Concomitant Pelvic-Floor-Muscle Contraction". Journal of Sport Rehabilitation 22, n. 2 (maggio 2013): 108–14. http://dx.doi.org/10.1123/jsr.22.2.108.
Abstract (sommario):
Context:Coactivation of abdominal and pelvic-floor muscles (PFM) is an issue considered by researchers recently. Electromyography (EMG) studies have shown that the abdominal-muscle activity is a normal response to PFM activity, and increase in EMG activity of the PFM concomitant with abdominal-muscle contraction was also reported.Objective:The purpose of this study was to compare the changes in EMG activity of the deep abdominal muscles during abdominal-muscle contraction (abdominal hollowing and bracing) with and without concomitant PFM contraction in healthy and low-back-pain (LBP) subjects.Design:A 2 × 2 repeated-measures design.Setting:Laboratory.Participants:30 subjects (15 with LBP, 15 without LBP).Main Outcome Measures:Peak rectified EMG of abdominal muscles.Results:No difference in EMG of abdominal muscles with and without concomitant PFM contraction in abdominal hollowing (P = .84) and abdominal bracing (P = .53). No difference in EMG signal of abdominal muscles with and without PFM contraction between LBP and healthy subjects in both abdominal hollowing (P = .88) and abdominal bracing (P = .98) maneuvers.Conclusion:Adding PFM contraction had no significant effect on abdominal-muscle contraction in subjects with and without LBP.
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Ridzuan, Nursyazana, Aizreena Azaman, Soeed K, Izwyn Zulkapri e Asnida Abd Wahab. "Evaluation of muscle fatigue using infrared thermal imaging technique with assisted electromyography". Malaysian Journal of Fundamental and Applied Sciences 13, n. 4-2 (17 dicembre 2017): 509–14. http://dx.doi.org/10.11113/mjfas.v13n4-2.823.
Abstract (sommario):
Muscle fatigue in sports science is an established research area where various techniques and types of muscles have been studied in order to understand the fatigue condition. It can be used as an indicator for predicting muscle injury and other muscle problems which can decrease athletes’ performance. Muscle fatigue usually occurs after a long lasting or repeated muscular activity. Electromyography (EMG) assessment method is a standard tool used to evaluate muscle fatigue based on the signals from the neuromuscular activation during fatigue condition. However, additional time for equipment set up such as placement of the electrodes and the use of multiple wires make this overall setting a bit complicated. In addition, the signal from EMG which possessed some noise, need to be filtered and post processing time is also required to obtain a reliable measurement signal. Therefore, researchers have explored the application of thermal imaging technique as one of the alternative methods for muscle fatigue assessment. The objective of this study is to investigate the correlation of muscle fatigue condition measured using a non-invasive infrared thermal imaging technique and a standard evaluation method, EMG. Five healthy men were selected to run on a treadmill for 30 minutes with a constant speed setting. Temperature and EMG signals were registered from gastrocnemius muscle of the subjects' dominant leg simultaneously. Result obtained shows that the average temperature of gastrocnemius muscle decrease as subjects start to exercise. Further temperature decrease along with exercise and increase in temperature were observed during the recovery period. Statistical analysis was performed and analyzed using both temperature and EMG parameters. Result shows a significant strong correlation with r = 0.7707 and p < 0.05 between temperature difference and median frequency (MDF) for all subjects compared to average temperature. Therefore, it is concluded that temperature difference extracted from thermal images can be used as an ideal parameter for muscle fatigue evaluation.
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Dai, Yangyang, Feng Duan, Fan Feng, Zhe Sun, Yu Zhang, Cesar F. Caiafa, Pere Marti-Puig e Jordi Solé-Casals. "A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition". Entropy 23, n. 9 (6 settembre 2021): 1170. http://dx.doi.org/10.3390/e23091170.
Abstract (sommario):
An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
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Liu, Shing-Hong, Chuan-Bi Lin, Ying Chen, Wenxi Chen, Tai-Shen Huang e Chi-Yueh Hsu. "An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise". Sensors 19, n. 14 (14 luglio 2019): 3108. http://dx.doi.org/10.3390/s19143108.
Abstract (sommario):
In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.
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Zhang, Gong. "Detection and Extraction of Surface EMG Signal Based on Action Potential Sequence". Applied Mechanics and Materials 608-609 (ottobre 2014): 216–20. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.216.
Abstract (sommario):
Because volleyball athletes’ lower body and tendon strength all carry out overload operation in the intense training process, its long time exercise will bring pain for volleyball athletes. Therefore, to carry out lower body muscle dynamics research and its electromyography on volleyball players' muscle EMG changes analysis. While the establishment of volleyball players' lower body muscle kinetic model equations, to analyze of the muscle state characteristics from the dynamic perspective on the volleyball player batting process. And to establish the interaction between the lower body muscle and tendon, which can provide reference for protecting volleyball athletes’ lower body muscles.
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Bilyy, R. I. "Review of research towards the myoelectric method of controlling bionic prosthesis". Optoelectronic Information-Power Technologies 46, n. 2 (13 dicembre 2023): 142–49. http://dx.doi.org/10.31649/1681-7893-2023-46-2-142-149.
Abstract (sommario):
Myoelectric control of bionic prostheses is an important field of research in the field of rehabilitation. Intuitive and intelligent myoelectric control can restore upper limb function. However, much research now focuses on the development of various myoelectrical and biotechnical control methods, limiting research to the complex daily tasks of prosthetic manipulation, such as grasping and releasing. The article examines the latest advances in the research areas of bionic prosthesis management. In particular, attention is paid to the methods of determining movement intentions, classification of discrete movements, estimation of continuous movements, single-channel control, feedback control and combined control. Motor neurons group input signals from the central nervous system that affect muscles and form motor units. The electromyography (EMG) signal, which is obtained by recording motor neuron action potentials, reflects muscle activity. This signal, oscillating within ±5000 μV with a frequency of 6 to 500 Hz, reflects the characteristics of muscle contraction. Depending on the location of the sensors, EMG signals are divided into intramuscular and surface electromyography. Intramuscular electromyography provides an accurate study of muscle activation, but requires the implantation of sensors, which can lead to physical problems. EMG, which captures a signal from the surface of the skin, is easier to use and is widely used in experiments with myoelectric prostheses.
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Aljobouri, Hadeel K. "A Virtual EMG Signal Control and Analysis for Optimal Hardware Design". International Journal of Online and Biomedical Engineering (iJOE) 18, n. 02 (16 febbraio 2022): 154–66. http://dx.doi.org/10.3991/ijoe.v18i02.27047.
Abstract (sommario):
Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detection.Material and Methods: A novel virtual EMG signal control and analysis system design is proposed in this work. The system consists mainly of two parts, hardware and software toolbox. Hardware design is mainly dependent on using a muscle movement sensor as well as the feedback from the virtual toolbox. The virtual software design offers a relatively simple design of a friendly graphical user interface. It consists mainly of the input EMG signal and output signal after using different processing methods. Feedback response from the final EMG signal results after the processing may help the designer to present the optimal hardware design.Results: The output results show the output performance of the proposed virtual EMG data controlling and analysis with the implemented hardware design of the muscle sensor movement detection. The results show promise that these interfaces may provide a new option to benefit the designer in choosing the optimal prosthesis design of severely disabled persons.
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Sangaboina, Swathi. "IOT Enabled Wearable Gloves with SEMG Subsystem with Posture Analysis". International Journal for Research in Applied Science and Engineering Technology 9, n. 9 (30 settembre 2021): 1690–95. http://dx.doi.org/10.22214/ijraset.2021.38236.
Abstract (sommario):
Abstract: Electromyogram (EMG) is a technique to track the record , analyze and estimate the electrical activity produced by muscles. This technique is used to detect the muscle issues that harm the nerves activity , muscle tissues and identify the location where they are joined together . This paper discusses the implementation of a project which can be considered as a tool for the acquisition of muscle activity, presentation and real-time attainment of EMG signal using a specific EMG sensor. The live EMG reading is recorded using the Wi-Fi- enabled Raspberrypi and then sent to a remote server in our case ThingSpeak server with the help of IoT concepts which helps in the telemetry of the obtained biomedical signals using the cloud. Results are displayed in ThingSpeak. The live recordings are also obtained on the PC using the serial plotter. This project can also help us in monitor and observe the progress of the patient treatment even if the physiotherapist could not come and data can be directly sent to them. Thus, the project aims to develop an EMG monitoring device based on IoT, for analyzing and acquiring EMG signals. Keywords: EMG sensor, Raspberry pi, LCD, ADS1115
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Arunganseh, K., S. Sivakumaran, S. Kumaravel e P. A. Karthick. "ANALYSIS OF CORTICOMUSCULAR COHERENCE BETWEEN CORTICAL AND LOWER LIMB MUSCLE ACTIVITIES". Biomedical Sciences Instrumentation 57, n. 3 (15 luglio 2021): 378–85. http://dx.doi.org/10.34107/eoov1225.07378.
Abstract (sommario):
Stroke is one of the most common neurological disorders where the evaluation of functional connection between the motor cortex and muscle is essential. This corticomuscular control is usually determined by measuring coherence in the simultaneously recorded electroencephalography (EEG) - electromyography (EMG) activities. In this work, an attempt has been made to estimate the EEG-EMG coherence using Magnitude Squared Coherence function. For this purpose, the simultaneous EEG-EMG activities of ten healthy subjects during standing, level walking, stair descending, stair descending, ramp descending, and ramp ascending are considered. The EEG signals associated with the motor cortex region and EMG signal of Tibialis Anterior (TA) are subjected to magnitude squared coherence function. In addition, the interaction of conventional frequency bands of EEG, namely, alpha (8-12 Hz) and beta (14-30 Hz) spectral components with EMG signals are also analysed. The results show that there exists notable coherence between the electrical activities of brain and muscular system during various activities. In addition, the frequency band interactions are also found to be distinct for different activities. Therefore, it seems that the analysis could be extended for the evolution of corticomuscular functions in patients with stroke.
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Babu, R. Dhanush, Mahesh Veezhinathan, Dhanalakshmi Munirathnam e V. Aishwarya. "Generation of Pulse Sequence Using EMG Signals for Application in Transfemoral Prosthesis". IOP Conference Series: Materials Science and Engineering 1272, n. 1 (1 dicembre 2022): 012013. http://dx.doi.org/10.1088/1757-899x/1272/1/012013.
Abstract (sommario):
The percentage of people having a lower leg amputation is high, and the incidence of unemployment among these amputees is likewise rising. Hence, it requires the intervention of an innovative solution to serve the function of a lost limb. Electromyogram (EMG) signals is a result of the potential generated by muscles during contraction. In this work, an attempt has been made to extract EMG signals from four set of muscle groups and the acquired signals were pre-processed and transformed to pulses to extract the contraction phase of the signal. Furthermore, the processed signals were subject to feature extraction process where in the Mean Absolute Value (MAV), Integrated EMG Feature (IEMG) and various statistical parameters associated with the signal such as the mean, median, standard deviation, variance, kurtosis, skewness was calculated in order to serve as an input to drive the stepper motor of a transfemoral prosthesis. To promote real time acquisition and control, a transfemoral socket with an ischial containment has been designed.
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Satti, Afraiz Tariq, Jiyoun Kim, Eunsurk Yi, Hwi-young Cho e Sungbo Cho. "Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip". Sensors 21, n. 15 (27 luglio 2021): 5091. http://dx.doi.org/10.3390/s21155091.
Abstract (sommario):
Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.